Working GPU model
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3 changed files with 190 additions and 108 deletions
170
train_model.py
170
train_model.py
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@ -2,106 +2,168 @@ import os
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import json
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import numpy as np
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import cv2
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import argparse
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import tensorflow as tf
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from video_compression_model import NUM_CHANNELS, VideoCompressionModel, PRESET_SPEED_CATEGORIES
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from tensorflow.keras.callbacks import EarlyStopping
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from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
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print(tf.config.list_physical_devices('GPU'))
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print("GPUs Detected:", tf.config.list_physical_devices('GPU'))
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# Constants
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BATCH_SIZE = 8
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EPOCHS = 50
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TRAIN_SAMPLES = 5
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BATCH_SIZE = 16
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EPOCHS = 40
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LEARNING_RATE = 0.00001
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TRAIN_SAMPLES = 100
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MODEL_SAVE_FILE = "models/model.tf"
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MODEL_CHECKPOINT_DIR = "checkpoints"
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CONTINUE_TRAINING = None
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def load_list(list_path):
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with open(list_path, "r") as json_file:
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video_details_list = json.load(json_file)
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return video_details_list
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def load_frame_from_video(video_file):
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print("Extracting video frame...")
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cap = cv2.VideoCapture(video_file)
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ret, frame = cap.read()
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if not ret:
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return None
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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cap.release()
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return frame
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def preprocess(frame):
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return frame / 255.0
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def save_model(model, file):
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os.makedirs("models", exist_ok=True)
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model.save(os.path.join("models/", file))
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print("Model saved successfully!")
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def load_video_from_list(list_path):
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details_list = load_list(list_path)
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all_frames = []
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all_details = []
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num_videos = len(details_list)
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frames_per_video = int(TRAIN_SAMPLES / num_videos)
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print(f"Loading {frames_per_video} across {num_videos} videos")
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for video_details in details_list:
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VIDEO_FILE = video_details["video_file"]
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UNCOMPRESSED_VIDEO_FILE = video_details["uncompressed_video_file"]
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CRF = video_details['crf'] / 63.0
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PRESET_SPEED = PRESET_SPEED_CATEGORIES.index(video_details['preset_speed'])
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video_details['preset_speed'] = PRESET_SPEED
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frame = load_frame_from_video(os.path.join("test_data/", VIDEO_FILE))
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if frame is not None:
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all_frames.append(preprocess(frame))
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frames = []
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frames_compressed = []
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cap = cv2.VideoCapture(os.path.join("test_data/", VIDEO_FILE))
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cap_uncompressed = cv2.VideoCapture(os.path.join("test_data/", UNCOMPRESSED_VIDEO_FILE))
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for _ in range(frames_per_video):
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ret, frame_compressed = cap.read()
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ret_uncompressed, frame = cap_uncompressed.read()
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if not ret or not ret_uncompressed:
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continue
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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frame_compressed = cv2.cvtColor(frame_compressed, cv2.COLOR_BGR2RGB)
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frames.append(preprocess(frame))
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frames_compressed.append(preprocess(frame_compressed))
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for uncompressed_frame, compressed_frame in zip(frames, frames_compressed):
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all_details.append({
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"frame": frame,
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"frame": uncompressed_frame,
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"compressed_frame": compressed_frame,
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"crf": CRF,
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"preset_speed": PRESET_SPEED,
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"video_file": VIDEO_FILE
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})
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cap.release()
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cap_uncompressed.release()
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return all_details
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def preprocess(frame):
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return frame / 255.0
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def save_model(model):
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os.makedirs("models", exist_ok=True)
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model.save(MODEL_SAVE_FILE, save_format='tf')
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print("Model saved successfully!")
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def main():
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global BATCH_SIZE, EPOCHS, TRAIN_SAMPLES, LEARNING_RATE, CONTINUE_TRAINING
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# Argument parsing
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parser = argparse.ArgumentParser(description="Train the video compression model.")
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parser.add_argument('-b', '--batch_size', type=int, default=BATCH_SIZE, help='Batch size for training.')
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parser.add_argument('-e', '--epochs', type=int, default=EPOCHS, help='Number of epochs for training.')
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parser.add_argument('-s', '--training_samples', type=int, default=TRAIN_SAMPLES, help='Number of training samples.')
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parser.add_argument('-l', '--learning_rate', type=float, default=LEARNING_RATE, help='Learning rate for training.')
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parser.add_argument('-c', '--continue_training', type=str, nargs='?', const=MODEL_SAVE_FILE, default=None, help='Path to the saved model to continue training. If used without a value, defaults to the MODEL_SAVE_FILE.')
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args = parser.parse_args()
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# Use the parsed arguments in your script
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BATCH_SIZE = args.batch_size
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EPOCHS = args.epochs
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TRAIN_SAMPLES = args.training_samples
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LEARNING_RATE = args.learning_rate
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CONTINUE_TRAINING = args.continue_training
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print("Training configuration:")
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print(f"Batch size: {BATCH_SIZE}")
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print(f"Epochs: {EPOCHS}")
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print(f"Training samples: {TRAIN_SAMPLES}")
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print(f"Learning rate: {LEARNING_RATE}")
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print(f"Continue training from: {CONTINUE_TRAINING}")
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all_video_details_train = load_video_from_list("test_data/training.json")
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all_video_details_val = load_video_from_list("test_data/validation.json")
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model = VideoCompressionModel(NUM_CHANNELS)
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model.compile(loss='mean_squared_error', optimizer='adam')
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early_stop = EarlyStopping(monitor='val_loss', patience=3, verbose=1, restore_best_weights=True)
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# Prepare data
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all_train_frames = []
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all_val_frames = []
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all_crf_train = []
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all_crf_val = []
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all_preset_speed_train = []
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all_preset_speed_val = []
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for video_details_train, video_details_val in zip(all_video_details_train, all_video_details_val):
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all_train_frames.append(video_details_train["frame"])
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all_val_frames.append(video_details_val["frame"])
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all_crf_train.append(video_details_train['crf'])
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all_crf_val.append(video_details_val['crf'])
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all_preset_speed_train.append(video_details_train['preset_speed'])
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all_preset_speed_val.append(video_details_val['preset_speed'])
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all_train_frames = [video_details["frame"] for video_details in all_video_details_train]
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all_train_compressed_frames = [video_details["compressed_frame"] for video_details in all_video_details_train]
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all_val_frames = [video_details["frame"] for video_details in all_video_details_val]
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all_val_compressed_frames = [video_details["compressed_frame"] for video_details in all_video_details_val]
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all_crf_train = [video_details['crf'] for video_details in all_video_details_train]
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all_crf_val = [video_details['crf'] for video_details in all_video_details_val]
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all_preset_speed_train = [video_details['preset_speed'] for video_details in all_video_details_train]
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all_preset_speed_val = [video_details['preset_speed'] for video_details in all_video_details_val]
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# Convert lists to numpy arrays
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all_train_frames = np.array(all_train_frames)
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all_train_compressed_frames = np.array(all_train_compressed_frames)
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all_val_frames = np.array(all_val_frames)
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all_val_compressed_frames = np.array(all_val_compressed_frames)
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all_crf_train = np.array(all_crf_train)
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all_crf_val = np.array(all_crf_val)
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all_preset_speed_train = np.array(all_preset_speed_train)
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all_preset_speed_val = np.array(all_preset_speed_val)
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if CONTINUE_TRAINING:
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print("loading model:", CONTINUE_TRAINING)
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model = tf.keras.models.load_model(CONTINUE_TRAINING) # Load from the specified file
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else:
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model = VideoCompressionModel()
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# Define the optimizer with a specific learning rate
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optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)
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os.makedirs(MODEL_CHECKPOINT_DIR, exist_ok=True)
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checkpoint_callback = ModelCheckpoint(
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filepath=os.path.join(MODEL_CHECKPOINT_DIR, "epoch-{epoch:02d}.tf"),
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save_weights_only=False,
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save_best_only=False,
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verbose=1,
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save_format="tf"
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)
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print("\nTraining the model on frame pairs...")
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#tf.config.run_functions_eagerly(True)
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model.compile(loss='mean_squared_error', optimizer=optimizer)
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early_stop = EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True)
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print("\nTraining the model...")
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model.fit(
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{"frame": all_train_frames, "crf": all_crf_train, "preset_speed": all_preset_speed_train},
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all_val_frames, # Target is the compressed frame
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{"uncompressed_frame": all_train_frames, "compressed_frame": all_train_compressed_frames, "crf": all_crf_train, "preset_speed": all_preset_speed_train},
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all_train_compressed_frames, # Target is the compressed frame
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batch_size=BATCH_SIZE,
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epochs=EPOCHS,
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validation_data=({"frame": all_val_frames, "crf": all_crf_val, "preset_speed": all_preset_speed_val}, all_val_frames),
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callbacks=[early_stop]
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validation_data=({"uncompressed_frame": all_val_frames, "compressed_frame": all_val_compressed_frames, "crf": all_crf_val, "preset_speed": all_preset_speed_val}, all_val_compressed_frames),
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callbacks=[early_stop, checkpoint_callback]
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)
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print("\nTraining completed!")
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save_model(model, 'model.keras')
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save_model(model)
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if __name__ == "__main__":
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main()
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